People Like You
Contemporary Figures of Personalisation

People Like You

Personalisation is changing many parts of contemporary life, from the way we shop and communicate to the kinds of public services we access. We are told that purchases, experiences, treatments, and interactions can all be customised to an optimum.

As a group of scientists, sociologists, anthropologists and artists, we are exploring how personalisation actually works. What are optimum outcomes? Do personalising practices have unintended consequences?

We argue that personalisation is not restricted to a single area of life and that personalised practices develop, interact and move between different sites and times. The project is split into four areas: personalised medicine and care; data science; digital cultures; interactive arts practices.

People Like You: Contemporary Figures of Personalisation is funded by a Collaborative Award in the Medical Humanities and Social Sciences from The Wellcome Trust, 2018–2022.


The coronavirus pandemic has transformed so many things in our lives, from the way we work to the way we socialise. But the impact has not been experienced equally. While the whole of the UK population was asked to practice social distancing during the lockdown, one newly created category of people was asked to pay special care to reduce their own exposure to the disease: those who were identified as being at high risk from complications from COVID-19. These “clinically extremely vulnerable” people were asked to take action beyond normal social distancing to protect themselves from SARS-COV-2.

In a letter received by many people who were defined as clinically extremely vulnerable, they were informed that:

“The safest course of action is for you to stay at home at all times and avoid all face-to-face contact for at least 12 weeks from today, except from carers and healthcare workers who you must see as part of your medical care.”

The impact of the creation of this category, through the establishment of the Shielded Persons List (SPL) by NHS Digital, cannot be understated.  From the outset there have been questions around the effects that the shielding rules have had on the mental and physical wellbeing of people affected due to isolation, financial and practical difficulties.[1] In addition, the method of the list creation, application, communication and revision has also had major impacts on people. For many people who were, or were not, included on the SPL, and for many whose status changed, there has been confusion, uncertainty, mistrust, and feelings of vulnerability with regards to what actions they should take.

In our public involvement work many people who were shielding felt that they had to make decisions for themselves (or contact charities for advice) about whether they should be shielding.[2] This was often in the absence of support from their healthcare providers, whom they felt were busy with the COVID-19 response. The uptake of the guidance has varied substantially depending on how much individuals felt like they were appropriately categorised, and also whether they felt that the guidance matched their own risk perceptions. It has also meant that many people decided that they should have been included on the list and decided to shield themselves.

Part of this uncertainty of whether this category fits an individual was due to the way in which the list appears to be based in an automated algorithm, which feels impersonal or perhaps ill-suited when applied on the individual level. This may change with the application of a (currently being built) Oxford produced a predictive risk algorithm, which if deployed would mean that rather than strict categorisation based on diagnoses, risk will be identified by how people like you have reacted to SARS-COV-2.[3] This may make it look less like a “one-size-fits-all” approach and more “personalised”. Some of the uncertainty and mistrust can also be associated with the dynamic processes that underpin the list production. Unpacking these process helps to clarify why responses to this list have been so varied.

To create the SPL, NHS Digital first deployed expert clinicians to create a list of high-risk disease groups, and people were then assigned to the SPL if they had these conditions. Most people on the SPL were identified centrally through an algorithm that mapped the list of high-risk conditions onto individual level diagnoses categories. They “…‘translate’ (or map) the clinical requirements of the list into the right subsets of coded information so that individual patients could be identified.” Additional people were added to the SLP by GPs and secondary care following clinical guidance. The list is maintained centrally, but flows out to clinicians through local data systems, and can be updated by primary and secondary clinicians at the point of care.  Any updates are then taken into the national list on a weekly basis, which is then distributed the following week through the same channels.

This sounds quite organised, and in some ways makes intuitive sense: there are diseases and conditions that likely mean the people are likely to have worse reactions to COVID-19, so there is a national list created of people with these conditions using rules (an algorithm) created by experts to map conditions onto continually collected data.

An example of a rule used to map a condition to datasets (


After the initial list was created, clinical decision making by doctors who know the patients’ histories can override the algorithm.  In one GP interview about how the algorithm works in practice,

“The data – it gives you the false sense of precision because a code is a code…., but actually on a human level, there is something else going on. And the data will help us and it may be a very rough way to screen people, but somebody has to do another level of tailoring to the patient, to the individual.”

The same doctor said that they will also defer to the advice of a charity as to whether to add a flag to include a patient on the list. How a person finds out they are on the SPL is determined entirely by who adds them to the list, because responsibility to inform a person they are on the list lies with the person or body that adds the SPL flag to their file. They can be informed by their GP, secondary care, or NHS Digital. The dynamic nature of the list production, including the tailoring for the patient at the local level, has meant that the process can appear chaotic and impersonal to people who are impacted by these changes. The list, which is partially a product of their own health data, becomes unrecognisable as it is transformed and recontextualised for use in the COVID-19 era.

The  Oxford algorithm will further this transformation of patient data through the creation of a predictive risk algorithm, which will identify risk for severe response to COVID-19 based on likeness to other people’s clinical information, and how they responded to the disease. The idea that underpins this algorithm is that people who have data that is statistically similar to yours (people like you) will have similar responses to COVID-19.

The algorithm as described by the research protocol (Oxford University):

The original SPL… was developed early in the outbreak when there were very little data or evidence about the groups most at risk of poor COVID-19 outcomes, and so was intended to be a dynamic list that would adapt as our knowledge of the disease improved and more evidence became apparent… [we will] assess whether a predictive risk algorithm can be developed with the above evidence to permit a more sophisticated ‘risk stratification’ approach. There are a variety of potential uses for such a tool, but it is primarily anticipated that it could be used both clinically in informing patients of their individual risk category and managing them accordingly, and strategically to stratify the population for policy purposes.[4]

In our research at Imperial, some people have said that they would much prefer an individualised risk assessment, but it remains to be seen whether this new form of algorithmic personalisation will feel more appropriate than the current SPL category production process.

On August 1st, shielding was paused in England, Scotland and Northern Ireland, and paused August 16th in Wales. The pause in shielding has meant that many people have lost the protections and support that were provided because they were on the SPL, and those on the shielding list are no longer eligible for Statutory Sick Pay on the basis of being on this list. The way that shielding guidance is being applied is changing, for example at local level,  where people who are on the list can be informed that they need to shield if there are locally high rates of transmission. The list itself remains very much alive, waiting to be reactivated as the pandemic and policy continue to evolve. In the coming weeks as SAS-CoV-2 transmission increases, people who are considered vulnerable are likely to receive further communications about their risk, and it will be crucial to see whether the more personalised approach feels any more personal.



[1] Robb CE, de Jager CA, Ahmadi-Abhari S, Giannakopoulou P, Udeh-Momoh C, McKeand J, Price G, Car J, Majeed A, Ward H, Middleton L. Impact of social isolation on anxiety and depression during the early COVID-19 pandemic: a survey of older adults in London, UK. Frontiers in Psychiatry 2020;11:591120 doi: 10.3389/fpsyt.2020.591120

[2] Maria Piggin, Katherine Collet, Philippa Pristerà. Insight Report: Guidance for people who are clinically extremely vulnerable from COVID-19 (18 June 2020)

[3] Hippisley-Cox et al. 2020. “Development and evaluation of a tool for predicting risk of short-term adverse outcomes due to COVID-19 in the general UK population.” Research Protocol

[4] Hippisley-Cox et al. 2020. “Development and evaluation of a tool for predicting risk of short-term adverse outcomes due to COVID-19 in the general UK population.” Research Protocol




Day S., Lury, C.

Quantified: Biosensing Technologies in Everyday Life, 2016

This chapter argues that tracking involves an increasingly significant and diverse set of techniques in relation to the ongoing transformation of relations between observer and observed, and between observers. These developments include not only the proliferation of individual sensing devices associated with a growing variety of platforms, but also the emergence of new data infrastructures that pool, scale, and link data in ways that promote their repurposing. By means of examples ranging from genes and currencies to social media and the disappearance of an airplane, it is suggested that practices of tracking are creating new public-private distinctions in the dynamic problem space resulting from the analytics that pattern these data. These new distinctions are linked to changing forms of personhood and changing relations between market and state, economy and society.

Day, Sophie E. and Lury, Celia. 2016. Biosensing: Tracking Persons. In: Dawn Nafus, ed. Quantified: Biosensing Technologies in Everyday Life. Cambridge MA: MIT Press, pp. 43-66. ISBN 978-0-262-52875-7 [Book Section]